Diabetes mellitus (DM), often simply referred to as diabetes, is a group of metabolic diseases characterized by high glucose levels in the blood (i.e. hyperglycemia), either because the body does not produce enough insulin (Type 1 DM or T1DM), or because cells do not respond to the insulin that is produced (Type 2 DM or T2DM). Intensive treatment with insulin and with oral medications to maintain nearly normal levels of glycemia (i.e. euglycemia) markedly reduces chronic complications in both T1DM and T2DM [1,2,3], but may risk symptomatic hypoglycemia and potentially life-threatening severe hypoglycemia. Therefore, hypoglycemia has been identified as the primary barrier to optimal diabetes management [4,5]. People with T1DM and T2DM face a lifelong optimization problem: to maintain strict glycemic control without increasing their risk for hypoglycemia. However, the struggle for close glycemic control could result in large blood glucose (BG) fluctuations over time. This process is influenced by many external factors, including the timing and amount of insulin injected, food eaten, physical activity, etc. In other words, BG fluctuations in diabetes are the measurable result of the interactions of a complex and dynamic biological system, influenced by many internal and external factors.
The optimization of this system depends largely on self-treatment behavior, which has to be informed by glucose monitoring and has to utilize data and technology available in the field. The currently accessible data sources include self-monitoring of blood glucose (SMBG), continuous glucose monitoring (CGM), as well as assessment of symptoms and self-treatment practices. The available treatments include medication (exclusively for T2DM), multiple daily insulin injections (MDI), and insulin pumps (CSII—continuous subcutaneous insulin injection). Currently, these treatments are at various stages of development and clinical acceptance, with SMBG now a routine practice, CGM rapidly developing, and emerging integrated systems that combine CGM with CSII and pave the way for the artificial pancreas of the near future.
Self-Monitoring of Blood Glucose
Contemporary home BG meters offer convenient means for frequent and accurate BG determinations through SMBG [6,7]. Most meters are capable of storing BG readings (typically over 150 readings) and have interfaces to download these readings into a computing device such a PC. The meters are usually accompanied by software that has capabilities for basic data analysis (e.g. calculation of mean BG, estimates of the average BG over the previous two weeks, percentages in target, hypoglycemic and hyperglycemic zones, etc.), logging of the data, and graphical representations of the BG data (e.g. histograms, pie charts, etc.). In a series of studies we have shown that specific risk analysis of SMBG data could also capture long-term trends towards increased risk for hypoglycemia [8, 9,10], and could identify 24-hour periods of increased risk for hypoglycemia [11,12]. The basics of the risk analysis are presented below. The methods outlined here have been applied to both SMBG and CGM data.
Evaluating Risk for Hypoglycemia and Hyperglycemia:
These methods are based on the concept of Risk Analysis of BG data [13], and on the recognition of a specific asymmetry of the BG measurement scale that can be corrected by a mathematical data transformation [14]. The risk analysis steps are as follows:
1. Symmetrization of the BG Scale:
A nonlinear transformation is applied to the BG measurements scale to map the entire BG range (20 to 600 mg/dl, or 1.1 to 33.3 mmol/1) to a symmetric interval. The BG value of 112.5 mg/dl (6.25 mmol/1) is mapped to zero, corresponding to zero risk for hypo- or hyperglycemia. The analytical form of this transformation is f(BG, α,β)=[(ln(BG))α−β], α, β>0, where the parameters are estimated a-s α=1.084, β=5.381, γ=1.509, if BG is measured in mg/dl and α=1.026, β=1.861, γ=1.794 if BG is measured in mmol/l [14].
2. Assignment of a Risk Value to Each SMBG Reading:
We define the quadratic risk function r(BG)=10f(BG)2. The function r(BG) ranges from 0 to 100. Its minimum value is achieved at BG=112.5 mg/dl (a safe euglycemic BG reading), while its maximum is reached at the extreme ends of the BG scale. Thus, r(BG) can be interpreted as a measure of the risk associated with a certain BG level. The left branch of this parabola identifies the risk of hypoglycemia, while the right branch identifies the risk of hyperglycemia.
3. Computing Measures of Risk for Hypoglycemia and Glucose Variability:
Let x1, x2, . . . xn be a series of n BG readings, and let rl(BG)=r(BG) if f(BG)<0 and 0 otherwise; rh(BG)=r(BG) if f(BG)>0 and 0 otherwise. Then the Low Blood Glucose Index (LBGI) is computed as:
  LBGI  =            1      n        ⁢                  ∑                  i          =          1                n            ⁢                          ⁢              rl        ⁡                  (                      x            i                    )                    
In other words, the LBGI is a non-negative quantity that increases when the number and/or extent of low BG readings increases. In studies, the LBGI typically accounted for 40-55% of the variance of future significant hypoglycemia in the subsequent 3-6 months [8,9,10], which made it a potent predictor of hypoglycemia based on SMBG. Similarly, we compute the High Blood Glucose Index (HBGI) as follows:
  HBGI  =            1      n        ⁢                  ∑                  i          =          1                n            ⁢                          ⁢              rh        ⁡                  (                      x            i                    )                    
The HBGI is a non-negative quantity that increases when the number and/or extent of high BG readings increases.
Continuous Glucose Monitoring
Since the advent of continuous glucose monitoring technology 10 years ago [15,16,17], which initially had limited performance particularly in the hypoglycemic range [18,19], significant progress has been made towards versatile and reliable CGM devices that not only monitor the entire course of BG day and night, but also provide feedback to the patient, such as alarms when BG reaches preset low or high levels. A number of studies have documented the benefits of continuous glucose monitoring [20,21,22,23] and charted guidelines for clinical use and its future as a precursor to closed-loop control [24,25,26,27]. However, while CGM has the potential to revolutionize the control of diabetes, it also generates data streams that are both voluminous and complex. The utilization of such data requires an understanding of the physical, biochemical, and mathematical principles and properties involved in this new technology. It is important to know that CGM devices measure glucose concentration in a different compartment—the interstitium. Interstitial glucose (IG) fluctuations are related to BG presumably via the diffusion process [28,29,30]. To account for the gradient between BG and IG, CGM devices are calibrated with capillary glucose, which brings the typically lower IG concentration to corresponding BG levels. Successful calibration would adjust the amplitude of IG fluctuations with respect to BG, but would not eliminate the possible time lag due to BG-to-IG glucose transport and the sensor processing time (instrument delay). Because such a time lag could greatly influence the accuracy of CGM, a number of studies were dedicated to its investigation, yielding various results [31,32,33,34]. For example, it was hypothesized that if glucose fall is due to peripheral glucose consumption the physiologic time lag would be negative, i.e. fall in IG would precede fall in BG [28,35]. In most studies IG lagged behind BG (most of the time) by 4-10 minutes, regardless of the direction of BG change [30,31]. The formulation of the push-pull phenomenon offered reconciliation of these results and provided arguments for a more complex BG-IG relationship than a simple constant or directional time lag [34,36]. In addition, errors from calibration, loss of sensitivity, and random noise confound CGM data [37]. Nevertheless, the accuracy of CGM is increasing and may be reaching a physiological limit for subcutaneous glucose monitoring [38,39,40].
The Artificial Pancreas
The next step in the progression of diabetes management is automated glucose control, or the artificial pancreas, which links a continuous glucose monitor with an insulin pump. A key element of this combination is a closed-loop control algorithm or method, which monitors blood glucose fluctuations and the actions of the insulin pump, and recommends insulin delivery at appropriate times.
The artificial pancreas idea can be traced back to developments that took place over thirty years ago when the possibility for external BG regulation in people with diabetes had been established by studies using intravenous (i.v.) glucose measurement and i.v. infusion of glucose and insulin. Systems such as the Biostator™ have been introduced and used in hospital settings to maintain normoglycemia (or euglycemia) by exerting both positive (via glucose or glucagon) and negative (via insulin) control [51,52,53,54,55]. Detailed descriptions of the major early designs can be found in [56,57,58,59,60,61]. More work followed, spanning a broader range of BG control techniques, powered by physiologic mathematical modeling and computer simulation control [62,63,64,65]. A review of methods for i.v. glucose control can be found in [66]. However, i.v. closed-loop control remains cumbersome and unsuited for outpatient use. An alternative to extracorporeal i.v. control has been presented by implantable intra-peritoneal (i.p.) systems employing intravenous BG sampling and i.p. insulin delivery [67,68]. The implementation of these systems, however, requires considerable surgery. Thus, with the advent of minimally-invasive subcutaneous (s.c.) CGM, increasing academic and industrial effort has been focused on the development of s.c.-s.c. systems, using CGM coupled with an insulin infusion pump and a control algorithm or method [69,70,71,72]. In September 2006, the Juvenile Diabetes Research Foundation (JDRF) initiated the Artificial Pancreas Project and funded a consortium of centers to carry closed-loop control research [73]. So far, encouraging pilot results have been reported by several centers [74,75,76,77,78].
Thus, in the past 30 years the monitoring and control of BG levels in diabetes has progressed from assessment of average glycemia once in several months, through daily SMBG, to minutely CGM. The increasing temporal resolution of the monitoring technology has enabled increasingly intensive diabetes treatment, from daily insulin injections or oral medication, through insulin pump therapy, to the artificial pancreas of the near future.